Rod Pump monitoring is important to sustain acceptable productivity levels. An automated system for DC shape classification is desired for quicker response avoiding production disturbances. This project proposes a method for patterns recognition based on Artificial Neural Networks, so that DCs can be better classified by the used method.

1. DIAGNOSIS OF
ROD PUMP DOWNHOLE PROBLEMS
USING ARTIFICIAL NEURAL NETWORKS (ANN)
MODEL PROGRESS
ENG. RAMEZ MAHER
Ramez Maher- Cairo Univrsity M.sc candidate

2. PIPELINE
Define the right
data
Elliptical
Descriptors
Feature extraction
Pattern
recognition
Classifier
Testing and
Evaluation
- Pipelines are common in machine learning.
- Each Module has a big impact on the overall performance of a problem.
Ramez Maher- Cairo Univrsity M.sc candidate

3. DATASET
Human expert pre classification
Down hole Pump Problems No. of Cards
Normal 1863
Fluid Pound 4120
Gas Interference 30
LeakingTravellingValve 84
Pump Hitting Up 48
Pump Hitting Down 27
Data of 13 classes
Blocked Intake 28
Gas Lock 70
Rod Parted 17
Malfunctions in Tubing Anchor 14
Bend Polished Rod 47
Leaking StandingValve 21
Oil TooViscous 6
Total Number of Cards 6382
Ramez Maher- Cairo Univrsity M.sc candidate

4. ELLIPTICAL FOURIER DESCRIPTORS
The EFD provides a normalized set of coefficients that are rotation, translation and scale invariant.
letT be an arbitrary positive real number and let
C (t) : [0..T] → R2,C (t) = (x (t), y (t))
be a planar curve parameterized by t, such that C (t) ∈ C (2) .We can describe the curve in Equation
(1) using elliptic Fourier descriptors as follows:
𝑋 𝑡
𝑌 𝑡
= ෍
𝑖=0
𝑁 𝑎𝑖 𝑏𝑖
𝑐𝑖 𝑑𝑖
sin(
2𝑖𝜋𝑡
𝑇
)
cos(
2𝑖𝜋𝑡
𝑇
)
Ramez Maher- Cairo Univrsity M.sc candidate

5. EXAMPLE
PUMP HITTING BOTTOM (TAGGING ON BOTTOM)
Ramez Maher- Cairo Univrsity M.sc candidate

6. CLASSIFIER
Nets for elliptical
Fourier descriptors that
represent pump card
Input layer = 59 net
13 nets for classes of
pump conditions
Output layer = 13 net
Kolmogorov formula for hidden layer
P: No. of neurals in input layer
q: No. of neurals in output layer
a: constant (1-10 )
𝑚 = (𝑝 + 𝑞)0.5+𝑎
𝑚 = (59 + 13)0.5
+10
𝑚 = 18
Ramez Maher- Cairo Univrsity M.sc candidate

7. 1ST MODEL RESULTS
NO. OF EPOCHSTILL CONVERGENCE 1500
The total error : suggests that your
network had converged on a final
state and wouldn't improve much
with additional training.
Form figure it is shown that training
become stable.
Percent of Train Error = 0.02%
RMSE of Testing set = 0.0548%
Ramez Maher- Cairo Univrsity M.sc candidate

8. 1ST MODEL RESULTS
The Validation error : To Avoid
Overfitting .
15% of Input Data used forValidation.
Ramez Maher- Cairo Univrsity M.sc candidate